Manipulating a distributed agreement protocol to identify a desired set of storage units

ABSTRACT

A method includes obtaining, by a computing device, a plurality of sets of encoded data slices for storage in memory of a dispersed storage network (DSN). The method further includes identifying, by the computing device, a desired set of storage units within pools of storage units for storing the plurality of sets of encoded data slices. The method further includes generating, by the computing device, a specific source name based on the desired set of storage units and a distributed agreement protocol (DAP). The method further includes generating, by the computing device, a plurality of sets of slices names that includes the specific source name. The method further includes sending, by the computing device, a plurality of sets of write requests to the desired set of storage units regarding the plurality of sets of encoded data slices and in accordance with the plurality of sets of slice names.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

In a dispersed storage system that includes pluralities of storageunits, there are instances where it is more efficient, faster, and/ormore reliable for a computing device to write to a specific set ofstorage units rather than to other sets of storage units. In dispersedstorage systems that utilize a load-capacity balancing storage protocol,selection of specific sets of storage units is typically not permitted.Thus, a computing device is assigned a set of storage units to write to,which may not be the most efficient, fastest, or most reliable for thecomputing device.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of decentralized,or distributed, agreement protocol (DAP) in accordance with the presentinvention;

FIG. 10 is a schematic block diagram of an example of creatingpluralities of sets of slices in accordance with the present invention;

FIG. 11 is a schematic block diagram of an example of storage vaults inaccordance with the present invention; and

FIG. 12 is a logic diagram of an example of a method of manipulating aDAP to identify a desired set of storage units in accordance with thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 and 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data (e.g., data 40) as subsequently described withreference to one or more of FIGS. 3-8. In this example embodiment,computing device 16 functions as a dispersed storage processing agentfor computing device 14. In this role, computing device 16 dispersedstorage error encodes and decodes data on behalf of computing device 14.With the use of dispersed storage error encoding and decoding, the DSN10 is tolerant of a significant number of storage unit failures (thenumber of failures is based on parameters of the dispersed storage errorencoding function) without loss of data and without the need for aredundant or backup copies of the data. Further, the DSN 10 stores datafor an indefinite period of time without data loss and in a securemanner (e.g., the system is very resistant to unauthorized attempts ataccessing the data).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The managing unit 18 creates and stores user profile information (e.g.,an access control list (ACL)) in local memory and/or within memory ofthe DSN memory 22. The user profile information includes authenticationinformation, permissions, and/or the security parameters. The securityparameters may include encryption/decryption scheme, one or moreencryption keys, key generation scheme, and/or data encoding/decodingscheme.

The managing unit 18 creates billing information for a particular user,a user group, a vault access, public vault access, etc. For instance,the managing unit 18 tracks the number of times a user accesses anon-public vault and/or public vaults, which can be used to generate aper-access billing information. In another instance, the managing unit18 tracks the amount of data stored and/or retrieved by a user deviceand/or a user group, which can be used to generate a per-data-amountbilling information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment (i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 78 is shown inFIG. 6. As shown, the slice name (SN) 78 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of an embodiment of a decentralized,or distributed, agreement protocol (DAP) 80 that may be implemented by acomputing device, a storage unit, and/or any other device or unit of theDSN to determine where to store encoded data slices or where to findstored encoded data slices. The DAP 80 includes a plurality offunctional rating modules 81. Each of the functional rating modules 81includes a deterministic function 83, a normalizing function 85, and ascoring function 87.

Each functional rating module 81 receives, as inputs, a slice identifier82 and storage pool (SP) coefficients (e.g., a first functional ratingmodule 81-1 receives SP 1 coefficients “a” and b). Based on the inputs,where the SP coefficients are different for each functional ratingmodule 81, each functional rating module 81 generates a unique score 93(e.g., an alpha-numerical value, a numerical value, etc.). The rankingfunction 84 receives the unique scores 93 and orders them based on anordering function (e.g., highest to lowest, lowest to highest,alphabetical, etc.) and then selects one as a selected storage pool 86.Note that a storage pool includes one or more sets of storage units.Further note that the slice identifier 82 corresponds to a slice name orcommon attributes of set of slices names. For example, for a set ofencoded data slices, the slice identifier 82 specifies a data segmentnumber, a vault ID, and a data object ID, but leaves open ended, thepillar number. As another example, the slice identifier 82 specifies arange of slice names (e.g., 0000 0000 to FFFF FFFF).

As a specific example, the first functional rating module 81-1 receivesthe slice identifier 82 and SP coefficients for storage pool 1 of theDSN. The SP coefficients includes a first coefficient (e.g., “a”) and asecond coefficient (e.g., “b”). For example, the first coefficient is aunique identifier for the corresponding storage pool (e.g., SP #1's IDfor SP 1 coefficient “a”) and the second coefficient is a weightingfactor for the storage pool. The weighting factors are derived toensure, over time, data is stored in the storage pools in a fair anddistributed manner based on the capabilities of the storage units withinthe storage pools.

For example, the weighting factor includes an arbitrary bias whichadjusts a proportion of selections to an associated location such that aprobability that a source name will be mapped to that location is equalto the location weight divided by a sum of all location weights for alllocations of comparison (e.g., locations correspond to storage units).As a specific example, each storage pool is associated with a locationweight factor based on storage capacity such that, storage pools withmore storage capacity have a higher location weighting factor thanstorage pools with less storage capacity.

The deterministic function 83, which may be a hashing function, ahash-based message authentication code function, a mask generatingfunction, a cyclic redundancy code function, hashing module of a numberof locations, consistent hashing, rendezvous hashing, and/or a spongefunction, performs a deterministic function on a combination and/orconcatenation (e.g., add, append, interleave) of the slice identifier 82and the first SP coefficient (e.g., SU 1 coefficient “a”) to produce aninterim result 89.

The normalizing function 85 normalizes the interim result 89 to producea normalized interim result 91. For instance, the normalizing function85 divides the interim result 89 by a number of possible outputpermutations of the deterministic function 83 to produce the normalizedinterim result. For example, if the interim result is 4,325 (decimal)and the number of possible output permutations is 10,000, then thenormalized result is 0.4325.

The scoring function 87 performs a mathematical function on thenormalized result 91 to produce the score 93. The mathematical functionmay be division, multiplication, addition, subtraction, a combinationthereof, and/or any mathematical operation. For example, the scoringfunction divides the second SP coefficient (e.g., SP 1 coefficient “b”)by the negative log of the normalized result (e.g., e^(y)=x and/orln(x)=y). For example, if the second SP coefficient is 17.5 and thenegative log of the normalized result is 1.5411 (e.g., e^((0.4235))),the score is 11.3555.

The ranking function 84 receives the scores 93 from each of the functionrating modules 81 and orders them to produce a ranking of the storagepools. For example, if the ordering is highest to lowest and there arefive storage units in the DSN, the ranking function evaluates the scoresfor five storage units to place them in a ranked order. From theranking, the ranking module 84 selects one the storage pools 86, whichis the target for a set of encoded data slices.

The DAP 80 may further be used to identify a set of storage units, anindividual storage unit, and/or a memory device within the storage unit.To achieve different output results, the coefficients are changedaccording to the desired location information. The DAP 80 may alsooutput the ranked ordering of the scores.

FIG. 10 is a schematic block diagram of an example of creatingpluralities of sets of slices. Each plurality of sets of encoded dataslices (EDSs) corresponds to the encoding of a data object, a portion ofa data object, or multiple data object, where a data object is one ormore of a file, text, data, digital information, etc. For example, thehighlighted plurality of encoded data slices corresponds to a dataobject having a data identifier of “a2”.

Each encoded data slices of each set of encoded data slices is uniquelyidentified by its slice name, which is also used as at least part of theDSN address for storing the encoded data slice. As shown, a set of EDSsincludes EDS 1_1_1_a1 through EDS 5_1_1_a1. The EDS number includespillar number, data segment number, vault ID, and data object ID. Thus,for EDS 1_1_1_a1, it is the first EDS of a first data segment of dataobject “a1” and is to be stored, or is stored, in vault 1. Note thatvaults are a logical memory container supported by the storage units ofthe DSN. A vault may be allocated to one or more user computing devices.

As is further shown, another plurality of sets of encoded data slicesare stored in vault 2 for data object “b1”. There are Y sets of EDSs,where Y corresponds to the number of data segments created by segmentingthe data object. The last set of EDSs of data object “b1” includes EDS1_Y_2_b1 through EDS 5_Y_2_b1. Thus, for EDS 1_Y_2_b1, it is the firstEDS of the last data segment “Y” of data object “b1” and is to bestored, or is stored, in vault 2.

FIG. 11 is a schematic block diagram of an example of a plurality ofstorage pools (e.g., pool 1 through pool n) that support one or morestorage vaults. Each storage pool (e.g., 1 through n) includes one ormore sets of storage units, where the number of storage units in a setof storage unit corresponds to the pillar width number of sets ofencoded data slices it stores. For instance, storage pool 1 and storagepool 2 each include seven storage units and storage pool n includestwelve storage units. Note that a storage pool can have more or lessstorage units than illustrated and, from storage pool to storage pool,may have different numbers of storage units.

In this example, the storage pools 1 through n support three vaults(vault 1, vault 2 and vault 3). Vaults 1 and 2 use five of the storageunits and span multiple storage pools. Vault 3 uses seven of the storageunits and is only in storage pool “n”. The number of storage unitswithin a vault corresponds to the pillar width number, which is five forvaults 1 and 2 and seven for vault 3 in this example. Note that astorage pool may have rows of storage units, where SU #1 represents agroup of storage units, each corresponding to a first pillar number; SU#2 represents a second plurality of storage units, each corresponding toa second pillar number; and so on. For example, the box labeled storageunit SU #1 of storage pool 1 is representative of a plurality of storageunits.

Each device (e.g., computing devices 12-16, managing unit 18, integrityprocessing unit 20, storage unit) of the DSN may include the distributedagreement protocol 80 as shown in FIG. 9. The DAP 80 uses sliceidentifiers (e.g., the slice name and/or one or more common elementsthereof (e.g., the pillar number, the data segment number, the vault ID,and/or the data object ID)) to identify, for one or more sets of encodeddata slices, a set, or pool, of storage units. With respect to the threepluralities of sets of encoded data slices (EDSs) of FIG. 11, the DAP 80approximately equally distributes the sets of encoded data slicesthroughout the DSN memory (e.g., among the various storage units).

A computing device may target a set of storage units to store itsencoded data slices because, from the computing devices' perspective, itcan write more efficiently, reliably, and/or faster to the targeted setof storage units than a set assigned via the DAP. As described ingreater detail with reference to FIG. 12, the computing device canmanipulate the DAP such that the DAP assigns it the desired set ofstorage units. To do this, the computing device creates a specific sliceidentifier (e.g., a specific source name) instead of one that includesone or more randomly generated components (e.g., data object ID).

Over time, if many computing devices manipulate the DAP to get itsdesired set of storage units, a storage imbalance will likely occur. Thestorage imbalance (e.g., DAP imbalance) can be corrected, at least tosome degree, by the storage units. For example, the storage unitinitiate a change to the DAP coefficients (e.g., storage mappingcoefficients), which will cause some of the encoded data slices storedin the computing devices' desired sets of storage units to other storageunits. This will be discussed in greater detail with reference to FIG.12.

FIG. 12 is a logic diagram of an example of a method of manipulating adistributed agreement protocol (DAP) to identify a desired set ofstorage units. The method begins at step 100 where a computing device ofa dispersed storage network, obtains (e.g., receives, creates, etc.) aplurality of sets of encoded data slices for storage in memory of theDSN (e.g., the DSN memory includes a plurality of pools of storage units(e.g., a pool includes one or more sets of storage units) locatedthroughout a geographic area).

The method continues at step 102 where the computing device identifies adesired set of storage units within the plurality of pools of storageunits for storing the plurality of sets of encoded data slices. Forexample, the computing device identifies the desired set of storageunits within the plurality of pools of storage units having a desiredwriting speed (e.g., data rate and latency of writing that is betterthan a DAP identified set of storage units). As another example, thecomputing device identifies the desired set of storage units as a sethaving a desired efficiency (e.g., meeting and exceeding a writethreshold in a timely manner for a majority of the sets of encoded dataslices being stored). As yet another example, the computing deviceidentifies the desired set of storage units as the set having a desiredreliability (e.g., consistency of having at least a write thresholdnumber of storage units available for storing encoded data slices).

The manner in which the computing device identifies the desired set ofstorage units may be done in a variety of ways. For example, thecomputing device executes a lookup function (e.g., executing a lookupfunction to identify the set). As another example, the computing devicesinitiates a query that tests desired write speed, desired efficiency,and/or desired reliability and receiving a response thereto. As afurther example, the computing device accessing a historical record,which tracks the computing device's write speed, efficiency, and/orreliability data when writing to sets of storage units of the DSN. Fromthe historical record, the computing device selects the set of storeunits having the desired writing traits (e.g., desired write speed,desired efficiency, and/or desired reliability). As a still furtherexample, the computing device accesses a look up table to identify theset.

The method continues at step 104 where the computing device generates aspecific source name (e.g., a specific data object ID combined with avault ID) for the DAP to identify a desired set of storage units. Forexample, the computing device generates a specific data object ID basedon the DAP so that the DAP identifies the desired set storage units ofthe plurality of pools of storage units. The computing device generatesthe specific source name by generating a specific data object identifierand combining it with a vault identifier and/or revision levelinformation. Within the DSN, devices (e.g., the computing device, othercomputing devices, the storage units, managing unit, integrity unit,etc.) use the specific source name as the slice identifier whenexecuting the DAP to identify the desired set of storage units as thestorage units storing the plurality of sets of encoded data slices.

The method continues at step 106 where the computing device generates aplurality of sets of slices names for the plurality of sets of encodeddata slices. The computing device generates a slice name by combiningthe specific source name with a pillar number and a data segment number.The method continues at step 108 where the computing device sends, inaccordance with the plurality of sets of slice names, a plurality ofsets of write requests to the desired set of storage units regarding theplurality of sets of encoded data slices.

The method continues at step 110 where the storage units of the DSNdetermine whether there is a DAP imbalance. When the storage unitsdetermine there is not a DAP, the method loops back to step 100.

When there is a DAP imbalance, the method continues at step 112 wherethe storage units initiate an adjustment of the mapping coefficients.For example, one of the storage units adjusts the storage mappingcoefficients. As another example, a storage unit requests that themanaging unit adjust the storage mapping coefficients. The storage unitor managing until adjusts the storage mapping coefficients (e.g.,coefficient b for one or more functional rating modules of FIG. 9) suchthat one or more encoded data slices are transferred from storage unitsof the desired set of storage units to other storage units in the DSN.

The method continues at step 114 where the storage units execute the DAPusing the specific source name and the adjusted storage mappingcoefficients to identify one or more encoded data slices to betransferred to one or more other storage units. The method continues atstep 116 where the storage units of the desired set of storage unitstransfer the one or more encoded data slices to the one or more otherstorage units.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method comprises: obtaining, by a computingdevice of a dispersed storage network (DSN), a plurality of sets ofencoded data slices for storage in memory of the DSN, wherein the memoryof the DSN includes a plurality of pools of storage units locatedthroughout a geographic area, wherein a data object is divided into aplurality of data segments, and wherein the plurality of data segmentsis dispersed storage error encoded into the plurality of sets of encodeddata slices; identifying, by the computing device, a desired set ofstorage units within the plurality of pools of storage units for storingthe plurality of sets of encoded data slices; generating, by thecomputing device, a specific source name based on the desired set ofstorage units and a distributed agreement protocol (DAP), wherein theDAP identifies a set storage units of the plurality of pools of storageunits based on a slice identifier and a plurality of storage mappingcoefficients and wherein, when a device within the DSN executes the DAP,the device utilizes the specific source name as the slice identifier toidentify the desired set of storage units; generating, by the computingdevice, a plurality of sets of slices names for the plurality of sets ofencoded data slices, wherein the plurality of sets of slices namesinclude the specific source name; and sending, by the computing device,a plurality of sets of write requests to the desired set of storageunits regarding the plurality of sets of encoded data slices and inaccordance with the plurality of sets of slice names.
 2. The method ofclaim 1, wherein the generating the specific source name comprises:generating a specific data object identifier; and combining the specificdata object identifier with one or more of a vault identifier andrevision level information to produce the specific source name.
 3. Themethod of claim 1, wherein generating a slice name of the plurality ofsets of slices names comprises: combining the specific source name witha pillar number and a data segment number to produce the slice name. 4.The method of claim 1, where the DAP comprises: a plurality of functionsoperable to generate one or more unique scores based on a plurality ofstorage pool coefficients and one or more slice identifierscorresponding to the plurality of sets of encoded data slices; and aranking function that processes the one or more unique scores toidentify a selected storage pool to store the plurality of sets ofencoded data slices.
 5. The method of claim 1 further comprises:determining, by at least some storage units of the plurality of pools ofstorage units, a DAP imbalance resulting from computing devices creatingsource names having specific data object identifiers instead of randomlygenerating data object identifiers; adjusting, by the at least somestorage units, one or more storage mapping coefficients of the pluralityof storage mapping coefficients to produce an adjusted plurality ofstorage mapping coefficients; executing, by the at least some storageunits, the DAP using the specific source name and the adjusted pluralityof storage mapping coefficients to identify one or more encoded dataslices of the plurality of sets of encoded data slices to be transferredto one or more other storage units within the plurality of pools ofstorage units; and transferring, by one or more storage units of thedesired set of storage units, the one or more encoded data slices to theone or more other storage units.
 6. The method of claim 1, wherein theidentifying the desired set of storage units comprises one or more of:identifying a set of storage units within the plurality of pools ofstorage units having a desired writing speed with respect to thecomputing device; identifying the set of storage units within theplurality of pools of storage units having a desired efficiency withrespect to the computing device; and identifying the set of storageunits within the plurality of pools of storage units having a desiredreliability with respect to the computing device.
 7. The method of claim6, wherein the identifying the desired set of storage units comprisesone or more of: executing a lookup; initiating a query; receiving aquery response; accessing a historical record; accessing a table; andreceiving a desired set of storage units list.
 8. A computer readablememory device comprises: a first memory element that stores operationalinstructions that, when executed by a computing device of a dispersedstorage network (DSN), causes the computing device to: obtain aplurality of sets of encoded data slices for storage in memory of theDSN, wherein the memory of the DSN includes a plurality of pools ofstorage units located throughout a geographic area, wherein a dataobject is divided into a plurality of data segments, and wherein theplurality of data segments is dispersed storage error encoded into theplurality of sets of encoded data slices; and identify a desired set ofstorage units within the plurality of pools of storage units for storingthe plurality of sets of encoded data slices; a second memory elementthat stores operational instructions that, when executed by thecomputing device, causes the computing device to: generate a specificsource name based on the desired set of storage units and a distributedagreement protocol (DAP), wherein the DAP identifies a set storage unitsof the plurality of pools of storage units based on a slice identifierand a plurality of storage mapping coefficients and wherein, when adevice within the DSN executes the DAP, the device utilizes the specificsource name as the slice identifier to identify the desired set ofstorage units; and generate a plurality of sets of slices names for theplurality of sets of encoded data slices, wherein the plurality of setsof slices names include the specific source name; and a third memoryelement that stores operational instructions that, when executed by thecomputing device, causes the computing device to: send a plurality ofsets of write requests to the desired set of storage units regarding theplurality of sets of encoded data slices and in accordance with theplurality of sets of slice names.
 9. The computer readable memory deviceof claim 8, wherein the second memory element further stores operationalinstructions that, when executed by the computing device, causes thecomputing device to generate the specific source name by: generating aspecific data object identifier; and combining the specific data objectidentifier with one or more of a vault identifier and revision levelinformation to produce the specific source name.
 10. The computerreadable memory device of claim 8, wherein the second memory elementfurther stores operational instructions that, when executed by thecomputing device, causes the computing device to generate a slice nameof the plurality of sets of slices names by: combining the specificsource name with a pillar number and a data segment number to producethe slice name.
 11. The computer readable memory device of claim 8, thesecond memory element that stores operational instructions that, whenexecuted by the computing device, causes the computing device to utilizethe DAP by performing: a plurality of functions operable to generate oneor more unique scores based on a plurality of storage pool coefficientsand one or more slice identifiers corresponding to the plurality of setsof encoded data slices; and a ranking function that processes the one ormore unique scores to identify a selected storage pool to store theplurality of sets of encoded data slices.
 12. The computer readablememory device of claim 8 further comprises: a fourth memory element thatstores operational instructions that, when executed by each of at leastsome storage units of the plurality of pools of storage units, causesthe at least some storage units to: determine a DAP imbalance resultingfrom computing devices creating source names having specific data objectidentifiers instead of randomly generating data object identifiers;adjust one or more storage mapping coefficients of the plurality ofstorage mapping coefficients to produce an adjusted plurality of storagemapping coefficients; a fifth memory element that stores operationalinstructions that, when executed by the at least some storage units,causes the at least some storage units to: execute the DAP using thespecific source name and the adjusted plurality of storage mappingcoefficients to identify one or more encoded data slices of theplurality of sets of encoded data slices to be transferred to one ormore other storage units within the plurality of pools of storage units;and transfer, by one or more storage units of the desired set of storageunits, the one or more encoded data slices to the one or more otherstorage units of the at least some storage units.
 13. The computerreadable memory device of claim 8, wherein the first memory elementfurther stores operational instructions that, when executed by thecomputing device, causes the computing device to identify the desiredset of storage units by one or more of: identifying a set of storageunits within the plurality of pools of storage units having a desiredwriting speed with respect to the computing device; identifying the setof storage units within the plurality of pools of storage units having adesired efficiency with respect to the computing device; and identifyingthe set of storage units within the plurality of pools of storage unitshaving a desired reliability with respect to the computing device. 14.The computer readable memory device of claim 13, wherein the firstmemory element further stores operational instructions that, whenexecuted by the computing device, causes the computing device toidentify the desired set of storage units by one or more of: executing alookup; initiating a query; receiving a query response; accessing ahistorical record; accessing a table; and receiving a desired set ofstorage units list.
 15. A computing device of a dispersed storagenetwork (DSN), wherein the computing device comprises: interface;memory; and a processing module operably coupled to the memory and theinterface, wherein the processing module is operable to: obtain aplurality of sets of encoded data slices for storage in memory of theDSN, wherein the memory of the DSN includes a plurality of pools ofstorage units located throughout a geographic area, wherein a dataobject is divided into a plurality of data segments, and wherein theplurality of data segments is dispersed storage error encoded into theplurality of sets of encoded data slices; identify a desired set ofstorage units within the plurality of pools of storage units for storingthe plurality of sets of encoded data slices; generate a specific sourcename based on the desired set of storage units and a distributedagreement protocol (DAP), wherein the DAP identifies a set storage unitsof the plurality of pools of storage units based on a slice identifierand a plurality of storage mapping coefficients and wherein, when adevice within the DSN executes the DAP, the device utilizes the specificsource name as the slice identifier to identify the desired set ofstorage units; generate a plurality of sets of slices names for theplurality of sets of encoded data slices, wherein the plurality of setsof slices names include the specific source name; and send a pluralityof sets of write requests to the desired set of storage units regardingthe plurality of sets of encoded data slices and in accordance with theplurality of sets of slice names.
 16. The computing device of claim 15,wherein the processing module is further operable to generate thespecific source name by: generating a specific data object identifier;and combining the specific data object identifier with one or more of avault identifier and revision level information to produce the specificsource name.
 17. The computing device of claim 15, wherein theprocessing module is further operable to generate a slice name of theplurality of sets of slices names by: combining the specific source namewith a pillar number and a data segment number to produce the slicename.
 18. The computing device of claim 15, wherein the processingmodule is further operable to utilize the DAP by performing: a pluralityof functions operable to generate one or more unique scores based on aplurality of storage pool coefficients and one or more slice identifierscorresponding to the plurality of sets of encoded data slices; and aranking function that processes the one or more unique scores toidentify a selected storage pool to store the plurality of sets ofencoded data slices.
 19. The computing device of claim 15, wherein theprocessing module is further operable to identify the desired set ofstorage units by one or more of: identifying a set of storage unitswithin the plurality of pools of storage units having a desired writingspeed with respect to the computing device; identifying the set ofstorage units within the plurality of pools of storage units having adesired efficiency with respect to the computing device; and identifyingthe set of storage units within the plurality of pools of storage unitshaving a desired reliability with respect to the computing device. 20.The computing device of claim 19, wherein the processing module isfurther operable to identify the desired set of storage units by one ormore of: executing a lookup; initiating a query; receiving a queryresponse; accessing a historical record; accessing a table; andreceiving a desired set of storage units list.